1. Field of the Invention
The present invention relates to a search engine, and, more specifically, to a system and method for identifying objects and content that have a maximum number of group-wise connections, and for revealing the evolution of the state of those connections over time.
2. Description of the Related Art
Search engines are information retrieval tools used to search through pre-indexed information or mine through data. The operation of most search engines, particularly online search engines, is fairly standard. First, information is discovered and indexed by the search engine. Next, a query is run against the indexed information. Lastly, the most relevant indexed information is presented to the user as search results. Variation among search engines most often arises from the algorithms used by the engine to find information and determine the relevance between the query and that information. Developers and programmers are constantly creating new algorithms to obtain the most relevant search results for any given query.
Some search engines are used to cluster or group together diffuse objects or content that have one or more similarities. These clustering engines typically group similar objects or content based upon detectable characteristics such as text, structure, or format. For example, some online clustering search engines index the words in multiple documents or websites and then weight the links between two or more documents (i.e. the engine clusters the documents) based upon the number of words shared between those documents, as well as the frequency with which those shared words appear in the document. The clustered documents thereby form a vast network of shared word nodes. Indeed, many prior methods have attempted to organize information by creating a group of interconnected elements or nodes.
However, current state-of-the-art clustering engines typically only attempt to group information or individuals by a single connection or single characteristic at a time. For example, the clustering engine above only uses the number and frequency of words in a text to cluster documents. Using this technique, there is no guarantee that the clustered documents have anything in common beyond the characteristics of the initial search or criteria.
Accordingly, there is a continued need for an affinity or clustering engine that guarantees that a group or cluster of objects or content will have an internal connection structure beyond that defined by the initial search string. This rich internal connection structure will ensure that the object or content grouping is much more relevant than a grouping created with previous techniques.
There is also a continued need for a clustering engine that functions in real-time or nearly real-time. Previous clustering engines typically use previously-indexed information to identify connections at the time of the user's query. Accordingly, there is a need for a connection engine that continuously monitors and updates connections.
There is also a need for an efficient and comprehensible method of communicating the results of a clustering engine to a user. Although prior methods have included the communication of search results, these communication methods are primarily limited to textual presentation of results. These prior methods do not provide the most efficient or adaptable means for allowing the user to fully explore the results of the search or clustering engine.